Data assimilation (DA), as an indispensable component within contemporary
Numerical Weather Prediction (NWP) systems, plays a crucial role in generating
the analysis that significantly impacts forecast performance. Nevertheless, the
development of an efficient DA system poses significant challenges,
particularly in establishing intricate relationships between the background
data and the vast amount of multi-source observation data within limited time
windows in operational settings. To address these challenges, researchers
design complex pre-processing methods for each observation type, leveraging
approximate modeling and the power of super-computing clusters to expedite
solutions. The emergence of deep learning (DL) models has been a game-changer,
offering unified multi-modal modeling, enhanced nonlinear representation
capabilities, and superior parallelization. These advantages have spurred
efforts to integrate DL models into various domains of weather modeling.
Remarkably, DL models have shown promise in matching, even surpassing, the
forecast accuracy of leading operational NWP models worldwide. This success
motivates the exploration of DL-based DA frameworks tailored for weather
forecasting models. In this study, we introduces FuxiDA, a generalized DL-based
DA framework for assimilating satellite observations. By assimilating data from
Advanced Geosynchronous Radiation Imager (AGRI) aboard Fengyun-4B, FuXi-DA
consistently mitigates analysis errors and significantly improves forecast
performance. Furthermore, through a series of single-observation experiments,
Fuxi-DA has been validated against established atmospheric physics,
demonstrating its consistency and reliability.

该研究介绍了 FuxiDA，一种用于吸收卫星观测数据的 DL-based DA 框架，通过吸收来自风云 - 4B 上的先进准同步辐射成像仪（AGRI）的数据，Fuxi-DA 不断减小分析误差并显著提高预测性能。此外，通过一系列单一观测实验，验证了 Fuxi-DA 与已建立的大气物理模型的一致性和可靠性。